Logistic density-dependent growth of an Aurelia aurita polyps population

Melica, Valentina; Invernizzi, Sergio; Caristi, Gabriella

doi:10.1016/j.ecolmodel.2014.07.009

Cited by 27 publications

(38 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Coyne (1973) reported that A. aurita scyphistoma population grows following a trend toward a sigmoid curve. Based on the same experimental data set analyzed in the present study, Melica et al (2014) demonstrated that population growth of A. aurita s.l. scyphistomae can be modeled by the basic density-dependent logistic (Verhulst) ordinary differential equation and that polyp density grows until reaching what can be considered as the carrying capacity of the population (Melica et al 2014) (see Fig.…”

Section: Discussionmentioning

confidence: 85%

“…In general, polyp-to-polyp reproduction rates increase with temperature and food supply (Lucas 2001;Lucas et al 2012;Purcell et al 2012;Pascual et al 2014). Within optimal ranges of these two key parameters, Aurelia scyphistomae propagate almost exclusively through NMB and ST, colonizing bare substrate very rapidly (Han and Uye 2010;Schiariti et al 2014;Melica et al, 2014;present study). However, available substrate, i.e., room for expansion, soon reaches a limit.…”

Section: Discussionmentioning

confidence: 92%

“…The polyps and reproductive products were counted from photographs as described in Melica et al (2014) using the public domain image processing program ImageJ provided by WCIF (Abramoff et al 2004). The three counts per oyster were averaged.…”

Section: Methodsmentioning

confidence: 99%

“…There are several descriptions of the development of these modes (e.g., Berrill 1949;Kakinuma 1975;Gröndahl 1988;Vagelli 2007;Adler and Jarms 2009;Han and Uye 2010;Schiariti et al 2014;Melica et al 2014). However, environmental control over the number and type of reproduction modes displayed and their relative importance to polyp density (i.e., reproductive strategy, see Schiariti et al 2014) are very poorly understood.…”

Section: Introductionmentioning

confidence: 97%

“…scyphistomae reach highest densities at "warm" temperatures (ca. 20-25 °C) and with a "high" food supply (Willcox et al 2008;Han and Uye 2010;Schiariti et al 2014) colonizing available substrate mostly by means of non-motile buds (NMB) and stolons (ST) (Schiariti et al 2014;Melica et al 2014). In addition, density-dependent effects appear under space-limiting conditions, lowering the population growth of Aurelia sp.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Density-dependent effects control the reproductive strategy and population growth of Aurelia aurita s.l. scyphistomae

et al. 2015

Self Cite

View full text Add to dashboard Cite

Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Density-dependent effects control the reproductive strategy and population growth of Aurelia aurita s.l. scyphistomae

et al. 2015

Self Cite

View full text Add to dashboard Cite

Multi‐scale spatial dynamics of the Chesapeake Bay nettle, Chrysaora chesapeakei

Shahrestani

Liang

et al. 2020

Ecosphere

View full text Add to dashboard Cite

Untangling organisms' multi-scale spatial distributions is challenging due to their interactions with environments at multiple spatial and temporal scales. We deployed an Adaptive Resolution Imaging Sonar (ARIS) in a Chesapeake Bay sub-estuary to investigate multi-scale spatial distributions of the bay nettle (Chrysaora chesapeakei) in May-September of 2016 and 2017. Nettles were found to be dispersed in aggregations of multiple individuals. The average density of bay nettles in 2017 was higher than in 2016. Small aggregations (<5 m) were persistent in both years but only contributed <10% of total observed nettles. Large patches (>30 m) contributed~40% of the total observed nettles. Large patches were more common in creek habitat where nettle density was higher. Nettle density was found to hit a peak value once in 2016, while there were two density peaks in 2017. Different aggregation patterns were observed during the second peak period in 2017 in which the number of large patches increased dramatically. Within the surveyed waterscape, the spatial patterns were consistent over time with higher abundance in the source creek than in the river channel, which underscores that C. chesapeakei requires hard substrate in shallow creeks for its benthic polyp stage. Using the ratio between nettles in the creek and near creek mouth as a proxy for dispersal rate, more nettles were transported out of the creek in 2017 than in 2016. The increase in patch size and high dispersion rate in peak periods in 2017 suggests that individuals were moving out of the creek habitat as density increased. Results highlight the complex spatial structure of bay nettles, which has major impacts on density estimates and subsequently affects our understanding of jellyfish population dynamics and long-term trends.

show abstract

End-to-End Statistical Model Checking for Parametric ODE Models

Julien

Cantin

Delahaye

2022

Quantitative Evaluation of Systems

View full text Add to dashboard Cite

We propose a simulation-based technique for the verication of structural parameters in Ordinary Dierential Equations. This technique is an adaptation of Statistical Model Checking, often used to verify the validity of biological models, to the setting of Ordinary Dierential Equations systems. The aim of our technique is to search the parameter space for the parameter values that induce solutions that best t experimental data under variability, with any metrics of choice. To do so, we discretize the parameter space and use statistical model checking to grade each individual parameter value w.r.t experimental data. Contrary to other existing methods, we provide statistical guarantees regarding our results that take into account the unavoidable approximation errors introduced through the numerical resolution of the ODE system performed while simulating. In order to show the potential of our technique, we present its application to two case studies taken from the literature, one relative to the growth of a jellysh population, and another concerning a prey-predator model.

show abstract

Logistic density-dependent growth of an Aurelia aurita polyps population

Cited by 27 publications

References 26 publications

Density-dependent effects control the reproductive strategy and population growth of Aurelia aurita s.l. scyphistomae

Density-dependent effects control the reproductive strategy and population growth of Aurelia aurita s.l. scyphistomae

Multi‐scale spatial dynamics of the Chesapeake Bay nettle, Chrysaora chesapeakei

End-to-End Statistical Model Checking for Parametric ODE Models

Contact Info

Product

Resources

About